Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AnthonyAlcaraz/agentic-graph-rag-skills --skill federated-context-governancegit clone --depth 1 https://github.com/AnthonyAlcaraz/agentic-graph-rag-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/federated-context-governance)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/federated-context-governance"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/federated-context-governance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/federated-context-governance"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/federated-context-governance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00184 | $0.02311 |
| Opus 5.5 | $0.00074 | $0.00924 |
| Sonnet 5.5 | $0.00037 | $0.00462 |
| Haiku 4.5 | $0.00018 | $0.00231 |
Grade A, and why
federated-context-governance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Federated Context Governance
Overview
The tool-orchestration stack (discovery, selection, gateway, execution) assumes a single coherent context. That assumption breaks when orchestration scales from one developer to a team. When one developer configures an AI coding agent — a CLAUDE.md, installed skills, hooks — the result is coherent and personalized. When five developers do the same thing independently, the result is five divergent architectures: each agent receives different instructions, applies different patterns, and produces code shaped by different assumptions.
Marc Baselga documented this after deploying Claude Code across an engineering team; Ben Erez called it the "unexpected tax." Two developers asking their agents to "follow our coding standards" receive different standards if their contexts diverge. The fragmentation follows a predictable progression:
individual optimization -> silent divergence -> visible inconsistency
-> coordination overhead
The chapter's three solution architectures are not competing options but LAYERS of one federated architecture (Table 6-4), mapped to organizational scale:
| Scale | Layer | Tool | Mechanism |
|---|---|---|---|
| Team | Configuration as Code | APM (Meppiel) | versioned, composable skill/rule/prompt packages; apm install gives everyone the same base |
| Department | Shared Knowledge Layer | Nia Skills (Rakhmetzhanov) | a central indexed knowledge base any agent queries |
| Enterprise | Governance Control Plane | Runtime (Jarjoura) | business rules, constraints, ownership, decisions as infrastructure |
The architecture is FEDERATED, not centralized: teams own domain-specific context but inherit an organizational base encoding nonnegotiable standards (security policies, architectural constraints, code-review requirements, compliance rules). Jarjoura's diagnosis is the decisive one: "Context failure, not AI failure." Agents amplify whatever structure they receive; incomplete or inconsistent structure produces amplified ambiguity at the speed of token generation.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 181 lines · 184 tokens per session scan A c99d6d996b1e
federated-context-governance is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (28 stars, last pushed 3mo ago), licensed MIT. It adds 184 tokens to every session and 2,311 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
federated-context-governance compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| federated-context-governanceAnthonyAlcaraz/agentic-graph-rag-skills | Skill | 28 | 3mo ago | MIT | A | 184 | Claude Code, Codex |
| lemmalogJordyZomer/lemmalog | Skill | 346 | 1mo ago | MIT | A | 105 | Claude Code, Codex |
| brain-ai-memoryHahyun-Lee/brain-ai-memory | Skill | 5 | 18d ago | MIT | A | 39 | Codex |
| graph-askstevepridemore/graph-memory | Skill | 2 | 3d ago | MIT | A | 43 | Claude Code |
| ingest-audiostevepridemore/graph-memory | Skill | 2 | 3d ago | MIT | A | 56 | Claude Code |
| ingeststevepridemore/graph-memory | Skill | 2 | 3d ago | MIT | A | 62 | Claude Code |
| graph-backupstevepridemore/graph-memory | Skill | 2 | 3d ago | MIT | A | 25 | Claude Code, Codex |